Real-World Uplift Modelling with Significance-Based Uplift Trees
Real-World Uplift Modelling with Significance-Based Uplift Trees
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发表时间：
2012
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通讯作者：
N. J. Radcliffe;Patrick D. Surry
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作者：
N. J. Radcliffe;Patrick D. Surry
本文旨在记录目前的最先进的“隆起建模”的行为，直接从一个指定的治疗，如营销干预的结果建模的变化的做法。我们包括的SignificanceBased隆起树的细节，形成了唯一的打包隆起建模软件的核心，目前可用。该文件包括一个摘要的一些结果，已交付使用隆起模型在实践中，从需求刺激和客户保留的应用程序中得出的例子。它还调查和讨论的方法，隆起建模变量的选择，模型的建设，质量措施和战后评估，所有这些都需要从传统的响应建模不同的方法所涉及的每个主要阶段。
This paper seeks to document the current state of the art in ‘uplift modelling’—the practice of modelling the change in behaviour that results directly from a specified treatment such as a marketing intervention. We include details of the SignificanceBased Uplift Trees that have formed the core of the only packaged uplift modelling software currently available. The paper includes a summary of some of the results that have been delivered using uplift modelling in practice, with examples drawn from demand-stimulation and customer-retention applications. It also surveys and discusses approaches to each of the major stages involved in uplift modelling—variable selection, model construction, quality measures and postcampaign evaluation—all of which require different approaches from traditional response modelling.